mlsanityData to 14 Oct 2026

Efficient Estimation of Word Representations in Vector Space

arXiv 1301.3781 ↗First posted 16 Jan 2013cs.CLcited by 4,375

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Cited by 70 in LLM reasoning, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity task, and the results are compared to the previously best performing techniques based on different types of neural networks. We observe large improvements in accuracy at much lower computational cost, i.e. it takes less than a day to learn high quality word vectors from a 1.6 billion words data set. Furthermore, we show that these vectors provide state-of-the-art performance on our test set for measuring syntactic and semantic word similarities.

Key facts. Cited by 4,375 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 Jan 2013.

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Citing papers per year: 2018: 4; 2019: 1; 2020: 1; 2021: 961; 2022: 800; 2023: 823; 2024: 768; 2025: 661; 2026: 356.

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